{"dataset":{"id":"238","dataset_id":"nm000205","name":"RSVP collaborative BCI dataset from Zheng et al 2020","description":"This dataset contains EEG recordings from a collaborative brain-computer interface (BCI) study using rapid serial visual presentation (RSVP) for target image detection. Fourteen healthy subjects, organized into seven pairs, performed synchronized RSVP tasks distinguishing human from non-human images, generating p300-like event-related potentials. The dataset is a BIDS-formatted derivative converted using the MOABB toolbox from the original data by Zheng et al. (2020).","owner_user_id":19,"status":"active","github_repo":"nemarDatasets/nm000205","concept_doi":"10.82901/nemar.nm000205","latest_version_doi":"10.82901/nemar.nm000205.v1.0.3","created_at":"2026-03-24 01:48:15","updated_at":"2026-08-21 10:45:59","zenodo_concept_id":"20518147","is_sandbox":0,"visibility":"public","ezid_status":"public","enrichment_json":"{\n  \"version\": \"2.0\",\n  \"pipeline_stage\": \"validated\",\n  \"title\": \"RSVP collaborative BCI dataset from Zheng et al 2020\",\n  \"description\": \"This dataset contains EEG recordings from a collaborative brain-computer interface (BCI) study using rapid serial visual presentation (RSVP) for target image detection. Fourteen healthy subjects, organized into seven pairs, performed synchronized RSVP tasks distinguishing human from non-human images, generating p300-like event-related potentials. The dataset is a BIDS-formatted derivative converted using the MOABB toolbox from the original data by Zheng et al. (2020).\",\n  \"methods_description\": \"EEG was recorded using a Neuroscan Synamps2 system with 62 channels referenced to the vertex (Cz), following the standard 10-20 montage, at a sampling rate of 1000 Hz with 50 Hz line frequency. Subjects performed an RSVP paradigm involving visual target detection (human vs non-human images), with two sessions per subject and three runs per session. Data were bandpass filtered (2–30 Hz) and analyzed with spatial filtering methods including CSP, PCA, CAR, and TRCA, and classified using HDCA.\",\n  \"license\": \"CC-BY-4.0\",\n  \"dataset_type\": \"derivative\",\n  \"authors\": {\n    \"Li Zheng\": {},\n    \"Sen Sun\": {},\n    \"Hongze Zhao\": {},\n    \"Weihua Pei\": {},\n    \"Hongda Chen\": {},\n    \"Xiaorong Gao\": {},\n    \"Lijian Zhang\": {},\n    \"Yijun Wang\": {}\n  },\n  \"keywords\": [\n    {\n      \"term\": \"EEG\"\n    },\n    {\n      \"term\": \"Event-Related Potentials, P300\",\n      \"subject_scheme\": \"MeSH\",\n      \"scheme_uri\": \"https://id.nlm.nih.gov/mesh/\",\n      \"value_uri\": \"http://id.nlm.nih.gov/mesh/D018913\"\n    },\n    {\n      \"term\": \"brain-computer interface\"\n    },\n    {\n      \"term\": \"RSVP\"\n    },\n    {\n      \"term\": \"P300\"\n    },\n    {\n      \"term\": \"target image detection\"\n    },\n    {\n      \"term\": \"collaborative BCI\"\n    }\n  ],\n  \"related_identifiers\": [\n    {\n      \"identifier\": \"10.3389/fnins.2020.579469\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDerivedFrom\"\n    },\n    {\n      \"identifier\": \"https://github.com/nemarDatasets/nm000205\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"10.21105/joss.01896\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"References\"\n    },\n    {\n      \"identifier\": \"10.1038/s41597-019-0104-8\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"References\"\n    },\n    {\n      \"identifier\": \"https://nemar.org/dataset/nm000205\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    }\n  ],\n  \"resource_type_general\": \"Dataset\",\n  \"resource_type_specific\": \"EEG Dataset\",\n  \"modalities\": [\n    \"eeg\"\n  ],\n  \"sizes\": [\n    \"12.7 GB (99 files)\"\n  ],\n  \"formats\": [\n    \".bdf\",\n    \".json\",\n    \".mat\",\n    \".md\",\n    \".tsv\",\n    \".yaml\",\n    \".yml\"\n  ],\n  \"source_hash\": \"6c4e3abb8f728b3c66a7bb792eae0e68449a806f363fccf758626cafdd4f7101\"\n}","last_activity_at":"2026-08-16 13:29:41","source":null,"source_id":null,"subject_count":14,"modalities":"eeg","age_min":24.9,"age_max":24.9,"file_size":12753632133,"total_files":669,"tasks":"p300","metadata_columns_error":null,"staleness_warn_stage":null,"staleness_admin_notified_at":null,"authors":"Li Zheng, Sen Sun, Hongze Zhao, Weihua Pei, Hongda Chen, Xiaorong Gao, Lijian Zhang, Yijun Wang","license":"CC-BY-4.0","readme":"[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000205-blue)](https://doi.org/10.82901/nemar.nm000205)\n\n# RSVP collaborative BCI dataset from Zheng et al 2020\n\nRSVP collaborative BCI dataset from Zheng et al 2020.\n\n## Dataset Overview\n\n- **Code**: Zheng2020\n- **Paradigm**: p300\n- **DOI**: 10.3389/fnins.2020.579469\n- **Subjects**: 14\n- **Sessions per subject**: 2\n- **Events**: Target=2, NonTarget=1\n- **Trial interval**: [0, 1] s\n- **Runs per session**: 3\n- **File format**: MATLAB\n\n## Acquisition\n\n- **Sampling rate**: 1000.0 Hz\n- **Number of channels**: 62\n- **Channel types**: eeg=62\n- **Channel names**: FP1, FPz, FP2, AF3, AF4, F7, F5, F3, F1, Fz, F2, F4, F6, F8, FT7, FC5, FC3, FC1, FCz, FC2, FC4, FC6, FT8, T7, C5, C3, C1, Cz, C2, C4, C6, T8, TP7, CP5, CP3, CP1, CPz, CP2, CP4, CP6, TP8, P7, P5, P3, P1, Pz, P2, P4, P6, P8, PO7, PO5, PO3, POz, PO4, PO6, PO8, O1, CB1, Oz, O2, CB2\n- **Montage**: standard_1020\n- **Hardware**: Neuroscan Synamps2\n- **Reference**: vertex (Cz)\n- **Line frequency**: 50.0 Hz\n\n## Participants\n\n- **Number of subjects**: 14\n- **Health status**: healthy\n- **Age**: mean=24.9, min=23, max=29\n- **Gender distribution**: female=10, male=4\n- **Handedness**: all right-handed\n- **Species**: human\n\n## Experimental Protocol\n\n- **Paradigm**: p300\n- **Number of classes**: 2\n- **Class labels**: Target, NonTarget\n- **Trial duration**: 1.0 s\n- **Study design**: RSVP target detection (human vs non-human images); 14 subjects in 7 pairs, synchronized EEG recording\n- **Feedback type**: visual\n- **Stimulus type**: RSVP images\n- **Stimulus modalities**: visual\n- **Primary modality**: visual\n- **Mode**: offline\n\n## HED Event Annotations\n\nSchema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser\n\n```\n  Target\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Target\n\n  NonTarget\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Non-target\n\n```\n## Paradigm-Specific Parameters\n\n- **Detected paradigm**: p300\n- **Stimulus onset asynchrony**: 100.0 ms\n\n## Data Structure\n\n- **Trials**: {'target': 168, 'nontarget': 4032}\n- **Trials context**: per subject across both sessions\n\n## Signal Processing\n\n- **Classifiers**: HDCA\n- **Feature extraction**: SIM, CSP, TRCA, PCA\n- **Frequency bands**: bandpass=[2.0, 30.0] Hz\n- **Spatial filters**: SIM, CSP, PCA, CAR, TRCA\n\n## Cross-Validation\n\n- **Method**: holdout\n- **Evaluation type**: within_subject, cross_session\n\n## BCI Application\n\n- **Applications**: target_image_detection, collaborative_BCI\n- **Environment**: laboratory\n- **Online feedback**: True\n\n## Tags\n\n- **Pathology**: Healthy\n- **Modality**: ERP\n- **Type**: RSVP\n\n## Documentation\n\n- **DOI**: 10.3389/fnins.2020.579469\n- **License**: CC-BY-4.0\n- **Investigators**: Li Zheng, Sen Sun, Hongze Zhao, Weihua Pei, Hongda Chen, Xiaorong Gao, Lijian Zhang, Yijun Wang\n- **Institution**: Chinese Academy of Sciences\n- **Country**: CN\n- **Data URL**: https://figshare.com/articles/dataset/12824771\n- **Publication year**: 2020\n\n## References\n\nZheng, L., Sun, S., Zhao, H., et al. (2020). A Cross-Session Dataset for Collaborative Brain-Computer Interfaces Based on Rapid Serial Visual Presentation. Frontiers in Neuroscience, 14, 579469. https://doi.org/10.3389/fnins.2020.579469\nAppelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Hochenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software 4: (1896). https://doi.org/10.21105/joss.01896\n\nPernet, C. R., Appelhoff, S., Gorgolewski, K. J., Flandin, G., Phillips, C., Delorme, A., Oostenveld, R. (2019). EEG-BIDS, an extension to the brain imaging data structure for electroencephalography. 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